Overview

Dataset statistics

Number of variables15
Number of observations3677
Missing cells0
Missing cells (%)0.0%
Duplicate rows244
Duplicate rows (%)6.6%
Total size in memory538.4 KiB
Average record size in memory149.9 B

Variable types

Boolean2
Categorical5
Numeric8

Alerts

Transported has constant value ""Constant
Dataset has 244 (6.6%) duplicate rowsDuplicates
Cabin_deck is highly overall correlated with HomePlanetHigh correlation
Consumption_Basic is highly overall correlated with Consumption_High_End and 5 other fieldsHigh correlation
Consumption_High_End is highly overall correlated with Consumption_Basic and 5 other fieldsHigh correlation
CryoSleep is highly overall correlated with FoodCourt and 3 other fieldsHigh correlation
FoodCourt is highly overall correlated with Consumption_Basic and 4 other fieldsHigh correlation
HomePlanet is highly overall correlated with Cabin_deckHigh correlation
RoomService is highly overall correlated with Consumption_Basic and 1 other fieldsHigh correlation
ShoppingMall is highly overall correlated with Consumption_Basic and 2 other fieldsHigh correlation
Spa is highly overall correlated with Consumption_Basic and 4 other fieldsHigh correlation
VRDeck is highly overall correlated with Consumption_Basic and 4 other fieldsHigh correlation
VIP is highly imbalanced (88.1%)Imbalance
RoomService has 3313 (90.1%) zerosZeros
FoodCourt has 2988 (81.3%) zerosZeros
ShoppingMall has 3113 (84.7%) zerosZeros
Spa has 3171 (86.2%) zerosZeros
VRDeck has 3208 (87.2%) zerosZeros
Consumption_High_End has 2909 (79.1%) zerosZeros
Consumption_Basic has 2799 (76.1%) zerosZeros

Reproduction

Analysis started2024-05-07 11:31:46.745777
Analysis finished2024-05-07 11:32:10.644319
Duration23.9 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

CryoSleep
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size161.4 KiB
True
2518 
False
1159 
ValueCountFrequency (%)
True 2518
68.5%
False 1159
31.5%
2024-05-07T13:32:10.836253image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Destination
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size186.5 KiB
TRAPPIST-1e
2335 
55 Cancri e
1000 
PSO J318.5-22
342 

Length

Max length13
Median length11
Mean length11.186021
Min length11

Characters and Unicode

Total characters41131
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTRAPPIST-1e
2nd rowTRAPPIST-1e
3rd row55 Cancri e
4th rowTRAPPIST-1e
5th row55 Cancri e

Common Values

ValueCountFrequency (%)
TRAPPIST-1e 2335
63.5%
55 Cancri e 1000
27.2%
PSO J318.5-22 342
 
9.3%

Length

2024-05-07T13:32:11.158163image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-07T13:32:11.430184image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
trappist-1e 2335
38.8%
55 1000
16.6%
cancri 1000
16.6%
e 1000
16.6%
pso 342
 
5.7%
j318.5-22 342
 
5.7%

Most occurring characters

ValueCountFrequency (%)
P 5012
12.2%
T 4670
11.4%
e 3335
 
8.1%
S 2677
 
6.5%
- 2677
 
6.5%
1 2677
 
6.5%
5 2342
 
5.7%
2342
 
5.7%
A 2335
 
5.7%
I 2335
 
5.7%
Other values (13) 10729
26.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 41131
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
P 5012
12.2%
T 4670
11.4%
e 3335
 
8.1%
S 2677
 
6.5%
- 2677
 
6.5%
1 2677
 
6.5%
5 2342
 
5.7%
2342
 
5.7%
A 2335
 
5.7%
I 2335
 
5.7%
Other values (13) 10729
26.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 41131
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
P 5012
12.2%
T 4670
11.4%
e 3335
 
8.1%
S 2677
 
6.5%
- 2677
 
6.5%
1 2677
 
6.5%
5 2342
 
5.7%
2342
 
5.7%
A 2335
 
5.7%
I 2335
 
5.7%
Other values (13) 10729
26.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 41131
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
P 5012
12.2%
T 4670
11.4%
e 3335
 
8.1%
S 2677
 
6.5%
- 2677
 
6.5%
1 2677
 
6.5%
5 2342
 
5.7%
2342
 
5.7%
A 2335
 
5.7%
I 2335
 
5.7%
Other values (13) 10729
26.1%

VIP
Boolean

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size161.4 KiB
False
3618 
True
 
59
ValueCountFrequency (%)
False 3618
98.4%
True 59
 
1.6%
2024-05-07T13:32:11.682794image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

RoomService
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct174
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.107996
Minimum0
Maximum3478
Zeros3313
Zeros (%)90.1%
Negative0
Negative (%)0.0%
Memory size186.5 KiB
2024-05-07T13:32:11.960674image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile31
Maximum3478
Range3478
Interquartile range (IQR)0

Descriptive statistics

Standard deviation109.06432
Coefficient of variation (CV)8.3204421
Kurtosis432.1375
Mean13.107996
Median Absolute Deviation (MAD)0
Skewness18.071429
Sum48198.1
Variance11895.026
MonotonicityNot monotonic
2024-05-07T13:32:12.327017image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3313
90.1%
2 26
 
0.7%
1 26
 
0.7%
3 16
 
0.4%
8 11
 
0.3%
5 9
 
0.2%
4 8
 
0.2%
6 7
 
0.2%
30 6
 
0.2%
27 6
 
0.2%
Other values (164) 249
 
6.8%
ValueCountFrequency (%)
0 3313
90.1%
1 26
 
0.7%
1.077766015 1
 
< 0.1%
2 26
 
0.7%
3 16
 
0.4%
4 8
 
0.2%
5 9
 
0.2%
6 7
 
0.2%
7 3
 
0.1%
8 11
 
0.3%
ValueCountFrequency (%)
3478 1
< 0.1%
2416 1
< 0.1%
2233 1
< 0.1%
1719 1
< 0.1%
1577 1
< 0.1%
1339 1
< 0.1%
1006 1
< 0.1%
1004 1
< 0.1%
987 1
< 0.1%
962 1
< 0.1%

FoodCourt
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct620
Distinct (%)16.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean539.64078
Minimum0
Maximum29813
Zeros2988
Zeros (%)81.3%
Negative0
Negative (%)0.0%
Memory size186.5 KiB
2024-05-07T13:32:12.645922image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3733.2
Maximum29813
Range29813
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1995.6508
Coefficient of variation (CV)3.6981096
Kurtosis59.276098
Mean539.64078
Median Absolute Deviation (MAD)0
Skewness6.5240281
Sum1984259.1
Variance3982622
MonotonicityNot monotonic
2024-05-07T13:32:13.051927image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2988
81.3%
1 8
 
0.2%
2 7
 
0.2%
4 4
 
0.1%
3 4
 
0.1%
6 4
 
0.1%
30 3
 
0.1%
166 3
 
0.1%
716 3
 
0.1%
784 3
 
0.1%
Other values (610) 650
 
17.7%
ValueCountFrequency (%)
0 2988
81.3%
1 8
 
0.2%
2 7
 
0.2%
3 4
 
0.1%
4 4
 
0.1%
6 4
 
0.1%
7 1
 
< 0.1%
8 2
 
0.1%
9 2
 
0.1%
11 1
 
< 0.1%
ValueCountFrequency (%)
29813 1
< 0.1%
27723 1
< 0.1%
27071 1
< 0.1%
26830 1
< 0.1%
18481 1
< 0.1%
17958 1
< 0.1%
17901 1
< 0.1%
17687 1
< 0.1%
17432 1
< 0.1%
17394 1
< 0.1%

ShoppingMall
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct437
Distinct (%)11.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean166.93278
Minimum0
Maximum23492
Zeros3113
Zeros (%)84.7%
Negative0
Negative (%)0.0%
Memory size186.5 KiB
2024-05-07T13:32:13.464203image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile980.2
Maximum23492
Range23492
Interquartile range (IQR)0

Descriptive statistics

Standard deviation746.54144
Coefficient of variation (CV)4.472108
Kurtosis302.43738
Mean166.93278
Median Absolute Deviation (MAD)0
Skewness13.00622
Sum613811.85
Variance557324.12
MonotonicityNot monotonic
2024-05-07T13:32:13.791349image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3113
84.7%
1 15
 
0.4%
3 11
 
0.3%
5 9
 
0.2%
2 8
 
0.2%
9 5
 
0.1%
4 4
 
0.1%
15 4
 
0.1%
885 4
 
0.1%
13 4
 
0.1%
Other values (427) 500
 
13.6%
ValueCountFrequency (%)
0 3113
84.7%
1 15
 
0.4%
2 8
 
0.2%
3 11
 
0.3%
4 4
 
0.1%
5 9
 
0.2%
6 3
 
0.1%
7 3
 
0.1%
8 3
 
0.1%
9 5
 
0.1%
ValueCountFrequency (%)
23492 1
< 0.1%
12253 1
< 0.1%
9058 1
< 0.1%
7810 1
< 0.1%
7185 1
< 0.1%
7148 1
< 0.1%
7104 1
< 0.1%
6805 1
< 0.1%
6331 1
< 0.1%
6221 1
< 0.1%

Spa
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct242
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.917445
Minimum0
Maximum2279
Zeros3171
Zeros (%)86.2%
Negative0
Negative (%)0.0%
Memory size186.5 KiB
2024-05-07T13:32:14.130676image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile95
Maximum2279
Range2279
Interquartile range (IQR)0

Descriptive statistics

Standard deviation145.58065
Coefficient of variation (CV)5.617091
Kurtosis93.654741
Mean25.917445
Median Absolute Deviation (MAD)0
Skewness8.7609695
Sum95298.447
Variance21193.725
MonotonicityNot monotonic
2024-05-07T13:32:14.461205image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3171
86.2%
1 41
 
1.1%
2 34
 
0.9%
5 19
 
0.5%
3 17
 
0.5%
4 10
 
0.3%
10 9
 
0.2%
6 9
 
0.2%
12 8
 
0.2%
7 8
 
0.2%
Other values (232) 351
 
9.5%
ValueCountFrequency (%)
0 3171
86.2%
1 41
 
1.1%
2 34
 
0.9%
3 17
 
0.5%
4 10
 
0.3%
5 19
 
0.5%
6 9
 
0.2%
7 8
 
0.2%
8 7
 
0.2%
9 5
 
0.1%
ValueCountFrequency (%)
2279 1
< 0.1%
2241 1
< 0.1%
2121 1
< 0.1%
2008 1
< 0.1%
1953 1
< 0.1%
1743 1
< 0.1%
1679 1
< 0.1%
1517 1
< 0.1%
1433 1
< 0.1%
1366 1
< 0.1%

VRDeck
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct250
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.215892
Minimum0
Maximum4088
Zeros3208
Zeros (%)87.2%
Negative0
Negative (%)0.0%
Memory size186.5 KiB
2024-05-07T13:32:14.791605image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile118
Maximum4088
Range4088
Interquartile range (IQR)0

Descriptive statistics

Standard deviation199.64647
Coefficient of variation (CV)5.8349048
Kurtosis141.49176
Mean34.215892
Median Absolute Deviation (MAD)0
Skewness10.28976
Sum125811.84
Variance39858.714
MonotonicityNot monotonic
2024-05-07T13:32:15.193231image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3208
87.2%
1 32
 
0.9%
2 21
 
0.6%
5 15
 
0.4%
3 12
 
0.3%
4 11
 
0.3%
6 11
 
0.3%
7 10
 
0.3%
8 9
 
0.2%
12 7
 
0.2%
Other values (240) 341
 
9.3%
ValueCountFrequency (%)
0 3208
87.2%
1 32
 
0.9%
2 21
 
0.6%
3 12
 
0.3%
4 11
 
0.3%
5 15
 
0.4%
6 11
 
0.3%
7 10
 
0.3%
8 9
 
0.2%
9 7
 
0.2%
ValueCountFrequency (%)
4088 1
< 0.1%
3875 1
< 0.1%
3146 1
< 0.1%
2491 1
< 0.1%
2453 1
< 0.1%
2428 1
< 0.1%
2275 1
< 0.1%
2102 1
< 0.1%
1960 1
< 0.1%
1937 1
< 0.1%

Cabin_deck
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size186.5 KiB
G
1180 
F
963 
B
585 
C
470 
E
205 
Other values (3)
274 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3677
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowF
2nd rowG
3rd rowB
4th rowB
5th rowB

Common Values

ValueCountFrequency (%)
G 1180
32.1%
F 963
26.2%
B 585
15.9%
C 470
 
12.8%
E 205
 
5.6%
D 163
 
4.4%
A 110
 
3.0%
T 1
 
< 0.1%

Length

2024-05-07T13:32:15.544988image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-07T13:32:15.884482image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
g 1180
32.1%
f 963
26.2%
b 585
15.9%
c 470
 
12.8%
e 205
 
5.6%
d 163
 
4.4%
a 110
 
3.0%
t 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
G 1180
32.1%
F 963
26.2%
B 585
15.9%
C 470
 
12.8%
E 205
 
5.6%
D 163
 
4.4%
A 110
 
3.0%
T 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3677
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
G 1180
32.1%
F 963
26.2%
B 585
15.9%
C 470
 
12.8%
E 205
 
5.6%
D 163
 
4.4%
A 110
 
3.0%
T 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3677
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
G 1180
32.1%
F 963
26.2%
B 585
15.9%
C 470
 
12.8%
E 205
 
5.6%
D 163
 
4.4%
A 110
 
3.0%
T 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3677
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
G 1180
32.1%
F 963
26.2%
B 585
15.9%
C 470
 
12.8%
E 205
 
5.6%
D 163
 
4.4%
A 110
 
3.0%
T 1
 
< 0.1%

Group_size
Real number (ℝ)

Distinct8
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2017949
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size186.5 KiB
2024-05-07T13:32:16.299454image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile6
Maximum8
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.5861434
Coefficient of variation (CV)0.72038653
Kurtosis2.1093664
Mean2.2017949
Median Absolute Deviation (MAD)1
Skewness1.5758042
Sum8096
Variance2.515851
MonotonicityNot monotonic
2024-05-07T13:32:16.600109image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 1727
47.0%
2 799
21.7%
3 554
 
15.1%
4 250
 
6.8%
5 130
 
3.5%
7 95
 
2.6%
6 91
 
2.5%
8 31
 
0.8%
ValueCountFrequency (%)
1 1727
47.0%
2 799
21.7%
3 554
 
15.1%
4 250
 
6.8%
5 130
 
3.5%
6 91
 
2.5%
7 95
 
2.6%
8 31
 
0.8%
ValueCountFrequency (%)
8 31
 
0.8%
7 95
 
2.6%
6 91
 
2.5%
5 130
 
3.5%
4 250
 
6.8%
3 554
 
15.1%
2 799
21.7%
1 1727
47.0%

HomePlanet
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size186.5 KiB
Earth
1528 
Europa
1336 
Mars
813 

Length

Max length6
Median length5
Mean length5.1422355
Min length4

Characters and Unicode

Total characters18908
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEarth
2nd rowEarth
3rd rowEuropa
4th rowEuropa
5th rowEuropa

Common Values

ValueCountFrequency (%)
Earth 1528
41.6%
Europa 1336
36.3%
Mars 813
22.1%

Length

2024-05-07T13:32:17.002760image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-07T13:32:17.443744image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
earth 1528
41.6%
europa 1336
36.3%
mars 813
22.1%

Most occurring characters

ValueCountFrequency (%)
a 3677
19.4%
r 3677
19.4%
E 2864
15.1%
t 1528
8.1%
h 1528
8.1%
u 1336
 
7.1%
o 1336
 
7.1%
p 1336
 
7.1%
M 813
 
4.3%
s 813
 
4.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18908
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 3677
19.4%
r 3677
19.4%
E 2864
15.1%
t 1528
8.1%
h 1528
8.1%
u 1336
 
7.1%
o 1336
 
7.1%
p 1336
 
7.1%
M 813
 
4.3%
s 813
 
4.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18908
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 3677
19.4%
r 3677
19.4%
E 2864
15.1%
t 1528
8.1%
h 1528
8.1%
u 1336
 
7.1%
o 1336
 
7.1%
p 1336
 
7.1%
M 813
 
4.3%
s 813
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18908
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 3677
19.4%
r 3677
19.4%
E 2864
15.1%
t 1528
8.1%
h 1528
8.1%
u 1336
 
7.1%
o 1336
 
7.1%
p 1336
 
7.1%
M 813
 
4.3%
s 813
 
4.3%

Transported
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size186.5 KiB
1
3677 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3677
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 3677
100.0%

Length

2024-05-07T13:32:17.975071image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-07T13:32:18.325288image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1 3677
100.0%

Most occurring characters

ValueCountFrequency (%)
1 3677
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3677
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 3677
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3677
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 3677
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3677
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 3677
100.0%

Consumption_High_End
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct420
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73.241333
Minimum0
Maximum4090
Zeros2909
Zeros (%)79.1%
Negative0
Negative (%)0.0%
Memory size186.5 KiB
2024-05-07T13:32:18.701226image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile474.6
Maximum4090
Range4090
Interquartile range (IQR)0

Descriptive statistics

Standard deviation296.77087
Coefficient of variation (CV)4.0519588
Kurtosis53.955606
Mean73.241333
Median Absolute Deviation (MAD)0
Skewness6.5078772
Sum269308.38
Variance88072.947
MonotonicityNot monotonic
2024-05-07T13:32:19.253279image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2909
79.1%
1 20
 
0.5%
2 17
 
0.5%
3 14
 
0.4%
4 13
 
0.4%
6 13
 
0.4%
5 13
 
0.4%
11 12
 
0.3%
10 8
 
0.2%
17 8
 
0.2%
Other values (410) 650
 
17.7%
ValueCountFrequency (%)
0 2909
79.1%
1 20
 
0.5%
1.077766015 1
 
< 0.1%
2 17
 
0.5%
3 14
 
0.4%
4 13
 
0.4%
5 13
 
0.4%
6 13
 
0.4%
7 7
 
0.2%
8 7
 
0.2%
ValueCountFrequency (%)
4090 1
< 0.1%
3877 1
< 0.1%
3696 1
< 0.1%
3348 1
< 0.1%
3151 1
< 0.1%
2904 1
< 0.1%
2879 1
< 0.1%
2866 1
< 0.1%
2769.583829 1
< 0.1%
2652 1
< 0.1%

Consumption_Basic
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct765
Distinct (%)20.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean706.57356
Minimum0
Maximum29813
Zeros2799
Zeros (%)76.1%
Negative0
Negative (%)0.0%
Memory size186.5 KiB
2024-05-07T13:32:19.685901image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4315.4
Maximum29813
Range29813
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2132.4919
Coefficient of variation (CV)3.0180749
Kurtosis48.431956
Mean706.57356
Median Absolute Deviation (MAD)0
Skewness5.8140478
Sum2598071
Variance4547521.8
MonotonicityNot monotonic
2024-05-07T13:32:20.264399image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2799
76.1%
690 4
 
0.1%
717 4
 
0.1%
774 4
 
0.1%
809 4
 
0.1%
785 4
 
0.1%
893 4
 
0.1%
804 3
 
0.1%
925 3
 
0.1%
790 3
 
0.1%
Other values (755) 845
 
23.0%
ValueCountFrequency (%)
0 2799
76.1%
1 1
 
< 0.1%
2 1
 
< 0.1%
47 1
 
< 0.1%
52 1
 
< 0.1%
53 1
 
< 0.1%
107 1
 
< 0.1%
107.6886717 1
 
< 0.1%
171 1
 
< 0.1%
195 1
 
< 0.1%
ValueCountFrequency (%)
29813 1
< 0.1%
27726 1
< 0.1%
27071 1
< 0.1%
26830 1
< 0.1%
23858 1
< 0.1%
18481 1
< 0.1%
18057 1
< 0.1%
17901 1
< 0.1%
17687 1
< 0.1%
17432 1
< 0.1%

Age_group
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size186.5 KiB
Young adults
2031 
Minor
895 
Middle-aged
648 
Senior
 
103

Length

Max length12
Median length12
Mean length9.9518629
Min length5

Characters and Unicode

Total characters36593
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowYoung adults
2nd rowYoung adults
3rd rowMinor
4th rowYoung adults
5th rowMiddle-aged

Common Values

ValueCountFrequency (%)
Young adults 2031
55.2%
Minor 895
24.3%
Middle-aged 648
 
17.6%
Senior 103
 
2.8%

Length

2024-05-07T13:32:20.879807image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-07T13:32:21.253372image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
young 2031
35.6%
adults 2031
35.6%
minor 895
15.7%
middle-aged 648
 
11.4%
senior 103
 
1.8%

Most occurring characters

ValueCountFrequency (%)
u 4062
11.1%
d 3975
10.9%
n 3029
 
8.3%
o 3029
 
8.3%
l 2679
 
7.3%
g 2679
 
7.3%
a 2679
 
7.3%
t 2031
 
5.6%
s 2031
 
5.6%
Y 2031
 
5.6%
Other values (7) 8368
22.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 36593
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
u 4062
11.1%
d 3975
10.9%
n 3029
 
8.3%
o 3029
 
8.3%
l 2679
 
7.3%
g 2679
 
7.3%
a 2679
 
7.3%
t 2031
 
5.6%
s 2031
 
5.6%
Y 2031
 
5.6%
Other values (7) 8368
22.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 36593
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
u 4062
11.1%
d 3975
10.9%
n 3029
 
8.3%
o 3029
 
8.3%
l 2679
 
7.3%
g 2679
 
7.3%
a 2679
 
7.3%
t 2031
 
5.6%
s 2031
 
5.6%
Y 2031
 
5.6%
Other values (7) 8368
22.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 36593
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
u 4062
11.1%
d 3975
10.9%
n 3029
 
8.3%
o 3029
 
8.3%
l 2679
 
7.3%
g 2679
 
7.3%
a 2679
 
7.3%
t 2031
 
5.6%
s 2031
 
5.6%
Y 2031
 
5.6%
Other values (7) 8368
22.9%

Interactions

2024-05-07T13:32:07.301901image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:31:48.687389image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:31:51.278260image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:31:54.038557image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:31:56.263995image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:31:58.672519image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:32:01.801040image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:32:04.999832image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:32:07.552209image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:31:48.951735image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:31:51.574101image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:31:54.405307image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:31:56.642862image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:31:58.995701image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:32:02.107053image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:32:05.226922image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:32:07.790316image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:31:49.242130image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:31:51.935478image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:31:54.645963image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:31:57.027717image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:31:59.280257image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:32:02.434654image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:32:05.479717image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:32:08.015159image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:31:49.613889image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:31:52.205298image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:31:54.871999image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:31:57.261622image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:31:59.520776image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:32:02.776691image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:32:05.729393image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:32:08.324824image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:31:49.861552image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:31:52.488931image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:31:55.106588image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:31:57.496012image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:31:59.789710image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:32:03.237516image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:32:06.046735image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:32:08.754869image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:31:50.181767image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:31:52.791519image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:31:55.428802image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:31:57.850433image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:32:00.416265image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:32:03.729238image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:32:06.392098image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:32:09.058640image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:31:50.565368image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:31:53.330258image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:31:55.732030image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:31:58.120517image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:32:01.010479image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:32:04.071805image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:32:06.629278image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:32:09.328108image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:31:50.883470image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:31:53.729216image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:31:56.012566image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:31:58.388087image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:32:01.503395image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:32:04.363802image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:32:06.922611image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2024-05-07T13:32:21.600516image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Age_groupCabin_deckConsumption_BasicConsumption_High_EndCryoSleepDestinationFoodCourtGroup_sizeHomePlanetRoomServiceShoppingMallSpaVIPVRDeck
Age_group1.0000.1750.1120.0990.0280.0500.102-0.1810.1860.0890.1090.0860.0690.060
Cabin_deck0.1751.000-0.149-0.1730.2020.303-0.179-0.1610.852-0.053-0.006-0.2020.165-0.207
Consumption_Basic0.112-0.1491.0000.9180.4240.0720.876-0.1230.2330.5800.7240.7400.2360.714
Consumption_High_End0.099-0.1730.9181.0000.3520.0540.818-0.1050.1740.6390.6330.8030.2370.770
CryoSleep0.0280.2020.4240.3521.0000.085-0.703-0.0150.137-0.488-0.625-0.5870.087-0.562
Destination0.0500.3030.0720.0540.0851.0000.024-0.0360.3040.0470.051-0.0120.039-0.001
FoodCourt0.102-0.1790.8760.818-0.7030.0241.000-0.0810.2290.4510.4640.6980.2360.694
Group_size-0.181-0.161-0.123-0.105-0.015-0.036-0.0811.0000.235-0.104-0.140-0.0430.033-0.042
HomePlanet0.1860.8520.2330.1740.1370.3040.2290.2351.000-0.003-0.131-0.0260.154-0.036
RoomService0.089-0.0530.5800.639-0.4880.0470.451-0.104-0.0031.0000.5000.3660.0000.320
ShoppingMall0.109-0.0060.7240.633-0.6250.0510.464-0.140-0.1310.5001.0000.4280.0000.409
Spa0.086-0.2020.7400.803-0.587-0.0120.698-0.043-0.0260.3660.4281.0000.2060.599
VIP0.0690.1650.2360.2370.0870.0390.2360.0330.1540.0000.0000.2061.0000.142
VRDeck0.060-0.2070.7140.770-0.562-0.0010.694-0.042-0.0360.3200.4090.5990.1421.000

Missing values

2024-05-07T13:32:09.691406image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-07T13:32:10.300167image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

CryoSleepDestinationVIPRoomServiceFoodCourtShoppingMallSpaVRDeckCabin_deckGroup_sizeHomePlanetTransportedConsumption_High_EndConsumption_BasicAge_group
6FalseTRAPPIST-1eFalse42.01539.03.00.00.0F2Earth142.01542.0Young adults
7TrueTRAPPIST-1eFalse0.00.00.00.00.0G2Earth10.00.0Young adults
9True55 Cancri eFalse0.00.00.00.00.0B3Europa10.00.0Minor
10TrueTRAPPIST-1eFalse0.00.00.00.00.0B3Europa10.00.0Young adults
11False55 Cancri eFalse39.07295.0589.0110.0124.0B3Europa1273.07884.0Middle-aged
12FalseTRAPPIST-1eFalse73.00.01123.00.0113.0F1Mars1186.01123.0Young adults
14FalseTRAPPIST-1eFalse8.0974.012.02.07.0F1Earth117.0986.0Young adults
18TrueTRAPPIST-1eFalse0.00.00.00.00.0F1Mars10.00.0Middle-aged
19FalseTRAPPIST-1eFalse0.00.00.00.00.0G2Earth10.00.0Minor
28TrueTRAPPIST-1eFalse0.00.00.00.00.0C1Europa10.00.0Senior
CryoSleepDestinationVIPRoomServiceFoodCourtShoppingMallSpaVRDeckCabin_deckGroup_sizeHomePlanetTransportedConsumption_High_EndConsumption_BasicAge_group
8643True55 Cancri eFalse0.00.00.00.00.0E1Europa10.00.0Young adults
8644TrueTRAPPIST-1eFalse0.00.00.00.00.0E2Europa10.00.0Young adults
8645TrueTRAPPIST-1eFalse0.00.00.00.00.0E2Europa10.00.0Young adults
8646TrueTRAPPIST-1eFalse0.00.00.00.00.0G1Earth10.00.0Young adults
8647True55 Cancri eFalse0.00.00.00.00.0G1Earth10.00.0Young adults
8650TrueTRAPPIST-1eFalse0.00.00.00.00.0G1Earth10.00.0Young adults
8651FalseTRAPPIST-1eFalse0.00.00.00.00.0A3Europa10.00.0Minor
8653FalseTRAPPIST-1eFalse0.03208.00.02.0330.0A3Europa1332.03208.0Young adults
8656FalseTRAPPIST-1eFalse0.00.01872.01.00.0G1Earth11.01872.0Young adults
8658FalseTRAPPIST-1eFalse126.04688.00.00.012.0E2Europa1138.04688.0Middle-aged

Duplicate rows

Most frequently occurring

CryoSleepDestinationVIPRoomServiceFoodCourtShoppingMallSpaVRDeckCabin_deckGroup_sizeHomePlanetTransportedConsumption_High_EndConsumption_BasicAge_group# duplicates
226TrueTRAPPIST-1eFalse0.00.00.00.00.0G1Earth10.00.0Young adults175
208TrueTRAPPIST-1eFalse0.00.00.00.00.0F1Mars10.00.0Young adults174
125TruePSO J318.5-22False0.00.00.00.00.0G1Earth10.00.0Young adults109
224TrueTRAPPIST-1eFalse0.00.00.00.00.0G1Earth10.00.0Minor74
212TrueTRAPPIST-1eFalse0.00.00.00.00.0F2Mars10.00.0Young adults63
151TrueTRAPPIST-1eFalse0.00.00.00.00.0B2Europa10.00.0Young adults59
214TrueTRAPPIST-1eFalse0.00.00.00.00.0F3Mars10.00.0Minor53
104True55 Cancri eFalse0.00.00.00.00.0G1Earth10.00.0Young adults52
49True55 Cancri eFalse0.00.00.00.00.0B1Europa10.00.0Young adults49
231TrueTRAPPIST-1eFalse0.00.00.00.00.0G3Earth10.00.0Minor49